The present invention relates generally to methods of optimizing the performance of subterranean wells and, in an embodiment described herein, more particularly provides a method of optimizing fields, reservoirs and/or individual wells utilizing neural networks.
Production of hydrocarbons from a field or reservoir is dependent upon a wide variety of influencing parameters. In addition, a rate of production from a particular reservoir or zone is typically limited by the prospect of damage to the reservoir or zone, water coning, etc., which may diminish the total volume of hydrocarbons recoverable from the reservoir or zone. Thus, the rate of production should be regulated so that an acceptable return on investment is received while enhancing the ultimate volume of hydrocarbons recovered from the reservoir or zone.
The rate of production from a reservoir or zone is only one of many parameters which may affect the performance of a well system. Furthermore, if one of these parameters is changed, another parameter may be affected, so that it is quite difficult to predict how a change in a parameter will ultimately affect the well system performance.
It would be very advantageous to provide a method whereby an operator of a well system could conveniently predict how the well system's performance would respond to changes in various parameters influencing the well system's performance. Furthermore, it would be very advantageous for the operator to be able to conveniently determine specific values for the influencing parameters which would optimize the economic value of the reservoir or field.